In medical applications, such as ultrasound-guided surgery, it may be desirable to register a pre-operative image onto the ultrasound image generated by an ultrasound probe during the surgery. For example, features of interest, such as tumors and lesions, may not be visible in the intra-operative ultrasound image used for surgical guidance. However, these features may be clearly visible in pre-operative images, such as magnetic resonance (MR) and computerized tomography (CT), images. Because these features of interest are visible in the pre-operative images but not in the intra-operative ultrasound images, it is desirable to transcribe or map these features from the pre-operative images to the intra-operative images.
One conventional method for mapping pre-operative image features into intra-operative images involves an image-to-image mapping of the pre-operative image to the intra-operative image. One problem with performing image-to-image mappings is that the image-to-image mappings are typically slow because of the number of pixels or voxels that must be mapped between the two images. For example, some image-to-image mapping techniques can take between five minutes and two hours to converge. Such slow convergence is unsuitable for applications, such as surgical guidance, that require real time changes in the mappings. For example, during ultrasound-guided percutaneous liver surgery, a pre-operative MR or CT image may initially be manually aligned as an overlay with an ultrasound image. During surgery, the liver may move and/or deform when the patient moves or breathes. As a result, the ultrasound image becomes misaligned with the pre-operative image. Similarly, in brain surgery, the brain may settle due to changes in pressure during surgery caused by opening of the skull or tumor removal. These movements also cause the ultrasound image to become misaligned with the pre-operative image.
Current surgical guidance systems attempt to solve this misalignment problem using a joystick or other method that allows manual alignment between the pre-operative and intra-operative images. However, such alignment is rigid and does not account for target image deformation during surgery. In addition, manual re-alignments must be continuously performed during surgery for a subject.
Accordingly, in light of these difficulties associated with conventional methods for aligning pre-operative and intra-operative image data, there exists a need for improved, methods, systems, and computer program products for registration between blood vessel and tissue surface image data.